Publications

@inproceedings{ysak2024,
title = {Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development},
author = {Agnieszka Łysak and Marcin Luckner},
url = {http://dx.doi.org/10.1007/978-3-031-63783-4_9},
doi = {10.1007/978-3-031-63783-4_9},
issn = {16113349},
year = {2024},
date = {2024-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14838 LNCS},
pages = {103--117},
publisher = {Springer Nature Switzerland},
abstract = {Deep neural network models are commonly used in computer vision problems, e.g., image segmentation. Convolutional neural networks have been state-of-the-art methods in image processing, but new architectures, such as Transformer-based approaches, have started outperforming previous techniques in many applications. However, those techniques are still not commonly used in urban analyses, mostly performed manually. This paper presents a framework for the residential building semantic segmentation architecture as a tool for automatic urban phenomena monitoring. The method could improve urban decision-making processes with automatic city analysis, which is predisposed to be faster and even more accurate than those made by human researchers. The study compares the application of new deep network architectures with state-of-the-art solutions. The analysed problem is urban functional zone segmentation for the urban sprawl evaluation using targeted land cover map construction. The proposed method monitors the expansion of the city, which, uncontrolled, can cause adverse effects. The method was tested on photos from three residential districts. The first district has been manually segmented by functional zones and used for model training and evaluation. The other two districts have been used for automated segmentation by models' inference to test the robustness of the methodology. The test resulted in 98.2% accuracy.},
keywords = {Computer vision, Deep learning, SegFormer, Semantic segmentation, Transformers},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wilkowski2019,
title = {City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms},
author = {Artur Wilkowski and Ihor Mykhalevych and Marcin Luckner},
doi = {10.1007/978-3-030-13273-6_31},
issn = {21945357},
year = {2019},
date = {2019-01-01},
booktitle = {Advances in Intelligent Systems and Computing},
volume = {920},
pages = {326--336},
abstract = {In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.},
keywords = {Computer vision, Detection, Tracking, Traffic monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wilkowski2019b,
title = {City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms},
author = {Artur Wilkowski and Ihor Mykhalevych and Marcin Luckner},
doi = {10.1007/978-3-030-13273-6_31},
issn = {21945357},
year = {2019},
date = {2019-01-01},
booktitle = {Advances in Intelligent Systems and Computing},
volume = {920},
pages = {326--336},
abstract = {In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.},
keywords = {Computer vision, Detection, Tracking, Traffic monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wilkowski2016,
title = {Low-cost canoe counting system for application in a natural environment},
author = {Artur Wilkowski and Marcin Luckner},
doi = {10.1007/978-3-319-29357-8_61},
issn = {21945357},
year = {2016},
date = {2016-01-01},
booktitle = {Advances in Intelligent Systems and Computing},
volume = {440},
pages = {705--715},
abstract = {? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively.},
keywords = {Classification with rejection, Computer vision, Pattern recognition},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wilkowski2016b,
title = {Low-cost canoe counting system for application in a natural environment},
author = {Artur Wilkowski and Marcin Luckner},
doi = {10.1007/978-3-319-29357-8_61},
issn = {21945357},
year = {2016},
date = {2016-01-01},
booktitle = {Advances in Intelligent Systems and Computing},
volume = {440},
pages = {705--715},
abstract = {? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively.},
keywords = {Classification with rejection, Computer vision, Pattern recognition},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Rudzinski2013,
title = {Low-cost computer vision based automatic scoring of shooting targets},
author = {Jacek Rudzinski and Marcin Luckner},
doi = {10.1007/978-3-642-37343-5_19},
issn = {03029743},
year = {2013},
date = {2013-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7828 LNAI},
pages = {185--195},
abstract = {This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag.},
keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Rudzinski2013b,
title = {Low-cost computer vision based automatic scoring of shooting targets},
author = {Jacek Rudzinski and Marcin Luckner},
doi = {10.1007/978-3-642-37343-5_19},
issn = {03029743},
year = {2013},
date = {2013-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7828 LNAI},
pages = {185--195},
abstract = {This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag.},
keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Bagrowski2012,
title = {Comparison of Corner Detectors for Revolving},
author = {Grzegorz Bagrowski and Marcin Luckner},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_53},
doi = {10.1007/978-3-642-29347-4_53},
year = {2012},
date = {2012-01-01},
booktitle = {Artificial Intelligence and Soft Computing Lecture Notes in Computer Science},
pages = {459--467},
publisher = {Springer Berlin Heidelberg},
abstract = {The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead.},
keywords = {3d modeling, Computer vision, corner detectors},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Rudzinski2012,
title = {Automatic scoring of shooting targets with tournament precision},
author = {Jacek Rudzinski and Marcin Luckner},
doi = {10.3233/978-1-61499-105-2-324},
issn = {09226389},
year = {2012},
date = {2012-01-01},
booktitle = {Frontiers in Artificial Intelligence and Applications},
volume = {243},
pages = {324--334},
abstract = {This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved.},
keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Rudzinski2012b,
title = {Automatic scoring of shooting targets with tournament precision},
author = {Jacek Rudzinski and Marcin Luckner},
doi = {10.3233/978-1-61499-105-2-324},
issn = {09226389},
year = {2012},
date = {2012-01-01},
booktitle = {Frontiers in Artificial Intelligence and Applications},
volume = {243},
pages = {324--334},
abstract = {This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved.},
keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Bagrowski2012b,
title = {Comparison of Corner Detectors for Revolving},
author = {Grzegorz Bagrowski and Marcin Luckner},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_53},
doi = {10.1007/978-3-642-29347-4_53},
year = {2012},
date = {2012-01-01},
booktitle = {Artificial Intelligence and Soft Computing Lecture Notes in Computer Science},
pages = {459--467},
publisher = {Springer Berlin Heidelberg},
abstract = {The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead.},
keywords = {3d modeling, Computer vision, corner detectors},
pubstate = {published},
tppubtype = {inproceedings}
}